Although NCAR doesn’t issue official forecasts, our research and technology is used by operational forecasters—those who issue regular outlooks for the National Weather Service, Federal Aviation Administration, military weather services, or private industry. Making those predictions more accurate, and longer range, benefits humanity both physically and economically.

In addition to routine weather prediction, another important focus of NCAR research is prediction of severe storms, which can damage lives and property. For example, more than 1,000 thunderstorms rage across Earth's surface at any moment. They bring beneficial rains, but thunderstorms can also spawn lightning, tornadoes, hail, and flash floods. Many complex physical factors must be understood to predict which storms will turn violent. NCAR scientists and their collaborators pry into the heart of severe storms using aircraft, balloons, mobile radars, and computer models, with the goal of better understanding these events and increasing the warning times for affected locations.

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Some of the most threatening weather events—gusty thunderstorm winds, ice on highways and aircraft, turbulence aloft—are the toughest to predict. They may affect only a small area, and they can develop and dissipate within minutes.

Investigating the dynamics of weather systems with the aim of improving their prediction, estimating their limits of predictability, identifying the key physical processes that limit forecast skill, and developing improved methods of determining forecast skill at the mesoscale, is a major research effort at NCAR. For instance, a long-running line of research has clarified the factors that result in different types of thunderstorms. These types include weak, short-lived cells; narrow, fast-moving squall lines; isolated supercells that pack large hail or tornadoes; and mammoth storm clusters that dump swaths of heavy rain. Training tools are derived from this research, and now help forecasters decide how and when to warn the public as storms evolve.

A severe thunderstorm, cumulonimbus cloud, moves across the plains east of Denver on June 10, 2004. Heavy rain and large hail is falling in the background, dark blue or blue-green area. A downdraft of cool, moist air produced by the rain and hail is pushing toward the camera. The strong winds produce fragmented cumulus clouds known as fractocumulus or scud, lighter, lower clouds in foreground.

Most weather forecasts offered by the news media cover some part of the 1- to 10-day period. To produce these outlooks, forecasters examine the results from sophisticated models that simulate the weather up to 16 days out.

How many days into the future can we accurately predict weather?Despite their invaluable role in forecasting, computer models are still unable to depict many aspects of day-to-day weather. NCAR scientists explore the limits of predictability and how those limits can be extended, as well as how and when forecasters should go beyond model guidance.

One example of NCAR's push to better predict weather on this scale, is its participation in THORPEX, a long-term research program organized under the World Meteorological Organization's World Weather Research Program. THORPEX research seeks to accelerate improvements in the accuracy of high-impact, 1-14 day weather forecasts for the benefit of society, economy and environmental stewardship. THORPEX seeks to reduce and mitigate the effects of natural disasters on society by transforming timely and accurate weather forecasts into specific and definite information in support of decisions that produce the desired benefits.

Precipitation is a key element in weather forecasts. As part of the U.S. Weather Research Program, NCAR is working to improve forecasts of rain and snow, particularly in the situations where they matter most, such as a hurricane landfall. Among other findings, the team has gained new insight into repetitive summertime rainfall that can inundate parts of the United States over several days. Some of these multiday episodes appear to be sustained by processes yet to be depicted in models. This work is now being extended to other parts of the globe at risk from similar summertime episodes of heavy rain.

An improved forecasting model is under development by NCAR and collaborators. The Weather Research and Forecasting Model (WRF) bolsters understanding and prediction and promotes closer ties between researchers and forecasters. The WRF model is a community model, and as such, its code can be downloaded from the WRF website, and developed by researchers all over the world. This has lead to many exciting developments by researchers across the world, and even to 2 major physics cores for the model, the WRF-ARW (a version aimed at advanced research & development needs) and WRF-NMM (a version aimed predominanatly at the needs of operational forecasters).

Useful weather forecasts were once limited to a period of two or three days. Now, thanks to improved computer models, there is measurable skill more than a week ahead. However, if a forecast period is extended much further, the outlook becomes no better than chance, because of the chaos effect—small errors that grow over time. Even when seasonal forecasts are skillful (see seasons to years), it's impossible to say what weather will materialize on a given day several months out.

Scientists at NCAR are working to pin down the edge of predictability. It once appeared the limit was 10 days. Computer models can now project large-scale weather features up to 15 days out, although the skill dwindles to negligible levels at the far end of that range.

Ensemble techniques are a valuable way to extend forecast range and quality, especially for longer-range periods. At NCAR and elsewhere, ensembles are created using several simultaneous runs of the same model. Researchers randomly tweak the initial conditions for each run, spanning the range of error known to be present at the starting line. There is no way to tell in advance which of the 10 forecasts in an ensemble will wind up being closest to correct. Still, the actual weather usually ends up within the ensemble range. Forecast centers in several countries now produce ensembles as part of their standard lineup of products. Through retrospective studies of ensembles, scientists can gain insight on how model errors grow.

Longer-term forecasts of one to three weeks may gain skill as they focus on regime shifts, the transitions into and out of weather patterns that persist for a week or more. NCAR scientists are examining whether there might be preferred modes of the atmosphere, such as configurations of the jet stream that tend to be locked into place—a long-sought guide for extended forecasting. However, early results hint that such modes may not serve as a useful forecast tool. Scientists are also exploring how nonlinear events (those that grow more quickly than the features usually tracked by large-scale models) affect regime shifts and dictate the limits of predictability.

Some factors and complexities in predicting drought. The influence of ENSO (El Niño-Southern Oscillation) was discussed at a July 2007 NCAR colloquium.

Some of the most dramatic progress in forecasting has taken place on the scale known as seasonal to interannual. It's impossible to foresee how day-to-day weather will take shape in that time frame. But forecasters can now make useful conclusions up to a year or more in advance about the likelihood of warmer, colder, wetter, or drier conditions than average.

Adjusting research radar on a buoy. Much of the success in seasonal-to-interannual forecasting stems from research at NCAR and elsewhere, especially on the roles that El Niño and La Niña play in global climate. As recently as 1982, there was no observing or forecasting system designed specifically to capture these warmings and coolings in the tropical Pacific Ocean. Improved data from sensors mounted on buoys has enabled meteorologists to monitor the Pacific on a daily basis for signs of a developing El Niño or La Niña. Computer models are becoming increasingly skilled at projecting the evolution of these events.

NCAR scientists have also turned their attention to other ocean-atmosphere interactions that affect global climate for months at a time. One recent study explored a potential connection between sea-surface temperatures in the eastern tropical Atlantic and the development of El Niño in the Pacific. The tropical Atlantic may also play a role in the North Atlantic Oscillation, a highly variable pattern that affects wintertime temperatures and rainfall across the northeast United States, eastern Canada, and Europe. Yet another oscillation, this one affecting the spread of Arctic cold, may explain why many winters since 1980 have been unusually mild across much of the Northern Hemisphere.

To study these and other climate cycles, NCAR uses long-range computer models and sophisticated statistical analyses.

It is well known that the structure and evolution of precipitating weather systems depend strongly on the microphysics and, in particular, on the conversion of water to ice and vice versa. Such microphysical processes affect the dynamics of systems through their influence on the strength of updrafts, downdrafts, and cold outflows; they also directly affect important forecast parameters such as precipitation type and amount. Quantitative precipitation forecasts, which are a critical societal requirement, are highly sensitive to these microphysical properties and processes. Despite this, precipitation formation processes are currently not adequately represented in both weather and climate models. Especially uncertain is the treatment of water and ice phases and precipitation development. Physically based improvements to the model physics must be developed, particularly for the ice formation that accounts for much of the deficiency.

Illustration of research on the affects of continental versus maritime atmospheric conditions, and clear versus polluted air, on the formation of various forms of precipitation in different cloud types. Understanding the very small (microphysical) precipitation processes such as droplet formation, furthers our understanding of weather and climate systems, and improves weather forecasting.

Bad weather, or badly forecast weather, means far more than cancelled plans and personal inconvenience. The impact of the weather on our economy, safety, and environment can be severe both through extreme events (hurricanes, tornadoes, floods, drought, etc.) and just day-to-day fluctuations. In the U.S., estimates of average annual damage ($16 billion) and loss of life (1,500) are significant and impact every state. According to the Department of Commerce, 42 percent of our gross domestic product is in sectors that are affected by severe weather and climate. Conversely, good information about weather and climate can be used effectively to enhance economic activities and improve quality of life.

Researchers in NCAR's Mesoscale and Microscale Meteorology Division of the Earth and Sun Systems Laboratory, and in the Research Applications Laboratory, strive to understand the physical processes and genesis of such extreme weather, in order to protect human lives and property.

According to the Department of Commerce, 42 percent of our gross domestic product is in sectors that are affected by severe weather and climate.